Association between Conflict and Cholera in Nigeria and the Democratic Republic of the Congo

Cholera outbreaks contribute substantially to illness and death in low- and middle-income countries. Cholera outbreaks are associated with several social and environmental risk factors, and extreme conditions can act as catalysts. A social extreme known to be associated with infectious disease outbreaks is conflict, causing disruption to services, loss of income, and displacement. To determine the extent of this association, we used the self-controlled case-series method and found that conflict increased the risk for cholera in Nigeria by 3.6 times and in the Democratic Republic of the Congo by 2.6 times. We also found that 19.7% of cholera outbreaks in Nigeria and 12.3% of outbreaks in the Democratic Republic of the Congo were attributable to conflict. Our results highlight the value of providing rapid and sufficient assistance during conflict-associated cholera outbreaks and working toward conflict resolution and addressing preexisting vulnerabilities, such as poverty and access to healthcare.

for the DRC on a daily temporal scale and was provided at the finest spatial scale possible.
Conflict data was provided by the United Nations Office for the Coordination of Humanitarian Affairs's Humanitarian Data Exchange (HDX, 2020). The data included subnational conflict events for both countries on a fine spatial scale, given to the exact location in longitude/latitude. This was reported on a daily temporal scale and spanned from 1997 to 2020.
The data was also categorised by event type which included battles, explosions, protests, riots, strategic developments and violence against civilians. This was further sub-categorised within these groups and reported number of fatalities.
The study period was selected as Jan 1997 to May 2020, as these were the first and last reports in the conflict data. The spatial granularity of the analysis was to administrative level 1 (states for Nigeria and provinces for the DRC) and all data points that were reported on a finer spatial scale were attributed to the upper level. To be included in the analysis, the state/province had to report both outbreaks and conflicts during the study period, therefore 22 provinces were included for the DRC and 36 states for Nigeria.

Sensitivity Analysis
Alternative exposure end points to identify the effect of lag.
Five alternative exposure periods were tested from the original exposure period (1 week after the onset of exposure, lag 1) and were named lag periods due to the potential lag effect from conflict onset to cholera outbreaks, these included: 1. Lag 2 -Week of conflict onset + 2 weeks 2. Lag 4 -Week of conflict onset + 4 weeks 3. Lag 6 -Week of conflict onset + 6 weeks 4. Lag 8 -Week of conflict onset + 8 weeks 5. Lag 10 -Week of conflict onset + 10 weeks The sensitivity analysis was run on both a national and sub-national level and S1 and S2 Figs show additional swimmer plots of lag 10 and line plots of the temporal trends.

Equations Used to Calculate the Percentage Attributable Fraction
First the number of outbreaks attributable to conflicts, , for each province . Is estimated using the formula: Where + is the total duration of conflict exposure for the province (if no conflict in province , thus + = 0), is the rate of outbreak occurrence in a Poisson process in the absence of conflict, and IRR is the incidence rate ratio associated with exposure to conflict. With − being the number of outbreaks observed in the province during the un-exposed period and being the total period of observation, an estimator of is Based on ^ and , the total number of outbreaks observed, we can easily obtain the equivalent of the population attributable fraction, , which corresponds to the proportion of the total number of outbreaks in both countries that are attributable to conflicts (this is equivalent to the PAF obtained in classical epidemiological studies, but here population refers to the "population of provinces"):

Excluded Events
States/provinces removed as they did not report conflict and cholera in the study period .

Democratic Republic of Congo:
. . Imo -239 conflict events removed The data (datLong) was fit to the model as follows: clogit(event ~ exgr + strata (